In the same vein as the ecological study of ad injectors I reviewed back in June, this paper does an ecological analysis of domain parking. Domain parking is the industry term of art for the practice of registering a whole bunch of domain names you don’t have any particular use for but hope someone will buy, and while you wait for the buyer, sticking a website consisting entirely of ads in the space, usually with This domain is for sale! at the top in big friendly letters. Like many ad-driven online business models, domain parking can be mostly harmless or it can be a lead-in to outright scamming people and infesting their computers with malware, and the research question in this paper is, how bad does it get?

In order to answer that question, the authors carried out a modest-sized survey of 3000 parked domains, identified by trawling the DNS for name servers associated with 15 known parking services. (Also like many other online businesses, domain parking runs on an affiliate marketing basis: lots of small fry register the domains and then hand the keys over to big services that do the actual work of maintaining the websites with the ads.) All of these services engage in all of the abusive behavior one would expect: typosquatting, aggressive behavioral ad targeting, drive-by malware infection, and feeding visitors to scam artists and phishers. I do not come away with a clear sense of how common any of these attacks are relative to the default parking page of advertisements and links—they have numbers, but they’re not very well organized, and different sets of parking pages were used in each section (discussing a different type of abuse) which makes it hard to compare across sections.

I’m most interested in the last section of the paper, in which they develop a machine classifier that can distinguish parking pages from normal webpages, based on things like the amount of text that is and isn’t a hyperlink, number of external links, total volume of resources drawn from third-party sources, and so on. The bulk of this section is devoted to enumerating all of the features that they tested, but doesn’t do a terribly good job of explaining which features wound up being predictive. Algorithmic choices also seem a little arbitrary. They got 97.9% true positive rate and 0.5% false positive rate out of it, though, which says to me that this isn’t a terribly challenging classification problem and probably most anything would have worked. (This is consistent with the intuitive observation that you, a human, can tell at a glance when you’ve hit a parking page.)